Towards optimal sensitivity-based anonymization for big data

Mohammed Al-Zobbi, Seyed Shahrestani, Chun Ruan

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

5 Citations (Scopus)

Abstract

Datasets containing private and sensitive information are useful for data analytics. Data owners cautiously release such sensitive data using privacy-preserving publishing techniques. Personal re-identification possibility is much larger than ever before. For instance, social media has dramatically increased the exposure to privacy violation. One well-known technique of k-anonymity proposes a protection approach against privacy exposure. K-anonymity tends to find k equivalent number of data records. The chosen attributes are known as Quasi-identifiers. This approach may reduce the personal re-identification. However, this may lessen the usefulness of information gained. The value of k should be carefully determined, to compromise both security and information gained. Unfortunately, there is no any standard procedure to define the value of k. The problem of the optimal k-anonymization is NP-hard. In this paper, we propose a greedy-based heuristic approach that provides an optimal value for k. The approach evaluates the empirical risk concerning our Sensitivity-Based Anonymization method. Our approach is derived from the fine-grained access and business role anonymization for big data, which forms our framework.
Original languageEnglish
Title of host publicationProceedings of the 27th International Telecommunication Networks and Applications Conference (ITNAC 2017), 22-24 November 2017, Melbourne, Vic.
PublisherIEEE
Pages331-336
Number of pages6
ISBN (Print)9781509067961
DOIs
Publication statusPublished - 2017
EventInternational Telecommunication Networks and Applications Conference -
Duration: 22 Nov 2017 → …

Publication series

Name
ISSN (Print)2474-154X

Conference

ConferenceInternational Telecommunication Networks and Applications Conference
Period22/11/17 → …

Keywords

  • MapReduce (computer file)
  • access control
  • big data
  • computer networks
  • computer security

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